近25 年三北防护林工程区土地退化及驱动力分析

doi:10.11821/xb201205002

地理学报 ›› 2012, Vol. 67 ›› Issue (5): 589-598.doi: 10.11821/xb201205002

近25 年三北防护林工程区土地退化及驱动力分析

黄森旺^1,2, 李晓松¹, 吴炳方¹, 裴亮²

1. 中国科学院遥感应用研究所, 北京100101;
2. 辽宁工程技术大学测绘与地理科学学院, 阜新123000

收稿日期:2011-09-20 修回日期:2011-11-11 出版日期:2012-05-20 发布日期:2012-05-20
通讯作者: 李晓松(1981-), 男, 副研究员, E-mail: lixs@irsa.ac.cn
基金资助:
中国科学院知识创新工程重大项目(KZCX1-YW-08-03); 国家科技支撑项目(2011BAH23B04)

The Distribution and Drivers of Land Degradation in the Three-North Shelter Forest Region of China during 1982-2006

HUANG Senwang^1,2, LI Xiaosong¹, WU Bingfang¹, PEI Liang²

1. Institute of Remote Sensing Applications, CAS, Beijing 100101, China;
2. Department of Survey and Geography Science, Liaoning Project Technology University, Fuxin 123000, Liaoning, China

Received:2011-09-20 Revised:2011-11-11 Online:2012-05-20 Published:2012-05-20
Supported by:
The Key Project of Knowledge Innovation Program of the Chinese Academy of Sciences, No.KZCX1-YW-08-03; National Science and Technology Supporting Item, No.2011BAH23B04

摘要/Abstract

摘要： 以长时间序列的遥感数据NOAA/AVHRR NDVI 为基础, 利用Sen 趋势度与Mann-Kendall 分析法相结合的方式分析了三北防护林工程区1982 年到2006 年土地退化趋势,结合气候因子降雨,运用残差法模型评价了人类活动在工程区内土地退化中所起的作用。结果表明:区域内土地退化程度整体趋于减轻,即植被上升的区域大于下降的区域,13.00%的地区退化程度显著减轻,6.20%的地区退化程度显著加重。其中绝大多数省份的土地退化程度趋于减轻,尤以内蒙古、青海和新疆最为明显,只有甘肃省土地退化程度明显趋于加重。而人类活动对植被变化起显著正作用的占11.93%,显著负作用的为6.19%。这说明在干旱、半干旱区,由于降雨随时间的变化不显著,对植被变化的影响相对较为微弱,植被显著变化主要受人类活动的影响。

关键词: 土地退化, 遥感, Sen+Mann-Kendall, 残差法, 空间变化, 三北防护林

Abstract: It is important to conduct studies on spatio-temporal distribution and drivers based on remote sensing for quantitative estimation of large-scale regional land degradation. In this paper, we analyze the land degradation trend in the Three-North Shelter Forest region of China from 1982 to 2006, using the remotely-sensed data of NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) based on the Sen +Mann-Kendall trend analysis method, and assess the combined effect of human activities and precipitation on the regional land degradation by using the Residual Trends model. The results indicate that the land degradation have a declining trend as a whole, with the significant increase and the significant decrease being 13.00% and 17.29%, respectively. Land degradation in most of the provinces tends to be relatively slight, especially in Inner Mongolia, Qinghai and Xinjiang, while Gansu witnesses the opposite trend. Human activities contributed 11.93% to the significant vegetation increase, and 6.19% to the degradation. This means that in the arid or semi-arid regions, the temporal change of precipitation is not significant, so the obvious vegetation change is mainly affected by human activities.

Key words: land degradation, remote sensing, Sen + Mann-Kendall, Residual Trends method, spatial distribution, Three-North Shelter Forest

黄森旺, 李晓松, 吴炳方, 裴亮. 近25 年三北防护林工程区土地退化及驱动力分析[J]. 地理学报, 2012, 67(5): 589-598.

HUANG Senwang, LI Xiaosong, WU Bingfang, PEI Liang. The Distribution and Drivers of Land Degradation in the Three-North Shelter Forest Region of China during 1982-2006[J]. Acta Geographica Sinica, 2012, 67(5): 589-598.

参考文献

[1] Reynolds J F, Stafford-Smith M. Global desertification: Do humans create deserts?//Stafford-Smith M, Reynolds JF. Do Hmans Create Deserts? Berlin: Dahlem University Press, 2002: 1-22.
[2] UNCCD. United Nations Convention to Combat Desertification in Countries Experiencing Serious Drought and/orDesertification, Particularly in Africa. Paris, 1994: 241/27.
[3] Thomas D S G. Science and desertification debate. Journal of Arid Environments, 1997, 37: 599-608.
[4] FAO. The Research Report Released by Food Argriculture Organization July 2, 2008 at the Rome Headquarters.Roman, 2008.
[5] Dregne H E, Kassas M, Rozanov B. A new assessment of the world status of desertification. DesertificationControl Bulletin, 1991, 20: 6-19.
[6] UNDP. China Human Development Report: Economic Transition and the Role of Government. Beijing: ChinaFinance and Economics Publishing House, 1999: 63-65.
[7] Prince S D, Becker-Reshef I, Rishmawi K. Detection and mapping of long-term land degradation using local netproduction scaling: Application to Zimbabwe. Remote Sensing of Environment, 2009: 1046-1057.
[8] Herrmann S M, Anyamba A, Tucker C J. Recent trends in vegetation dynamics in the African Sahel and theirrelationship to climate. Global Environmental Change, 2005, 15: 394-404.
[9] Fuller D O, Prince S D, Astle W L. The influence of canopy strata on remotely sensed observations ofSavanna-woodlands. International Joural of Remote Sensing, 1997, 18: 2985-3009.
[10] Wessels K J, Prince S D, Frost P E et al. Assessing the effects of human-induced land degradation in the formerhomelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sensing of Environment,2004, 91: 47-67.
[11] Wessels K J, Prince S D, Zambatis N et al. Relationship between herbaceous biomass and 1 km advanced veryhigh resolution radiometer (AVHRR) NDVI in Kruger National Park, South Africa. International Journal ofRemote Sensing, 2006, 27: 951-973.
[12] Xu Xinggui, Jing Xiaoqing, Levy J K. The spatial and temporal distribution of land surface heat in China and thecauses of abnormal changes. Science in China: Series D, 2009, 39(11): 1530-1542. [徐兴奎, 金晓青, Levy J K. 中国区域陆表热力作用时空分布及异常变化区域的成因. 中国科学: D辑, 2009, 39(11): 1530-1542.]
[13] Xu D Y, Kang X W, Zhuang D F et al. Multi-scale quantitative assessment of the relative roles of climate changeand human activities in desertification: A case study of the Ordos Plateau, China. Journal of Arid Environments,2010, 74: 498-507.
[14] Envans J, Geerken R. Discrimination between climate and human-induced dryland degradation. Journal of AridEnvironments, 2004, 57: 535-554.
[15] Camberlin P, Martiny N, Philippon N et al. Determinants of the inter-annual relationships between remote sensedphotosynthetic activity and rainfall in tropical Africa. Remote Sensing of Environment, 2007, 106(2): 199-216.
[16] Weiss E, Marsh S E, Prirman E S. Application of NOAA-AVHRR NDVI time-series data to assess changes inSaudi Arabia's rangelands. International Journal of Remote Sensing, 2001, 22(6): 1005-1027.
[17] Wessels K J, Prince S D, Malherbe J et al. Can human-induced land degradation be distinguished from the effectsof rainfall variability? A case study in South Africa. Journal of Arid Environments, 2007, 68(2): 271-297.
[18] Cai Bofeng. Monitoring and evaluation of forestry ecological project based on remote sensing [D].Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences, 2008. [蔡博峰. 基于遥感的林业生态工程监测和评价研究[D]. 北京: 中国科学院遥感应用研究所, 2008.]
[19] Forestry Bureau of Three-North. The Construction Master Plan of Chinese Three-North Shelterbelt Forest System.Yinchuan: Ningxia People's Publishing House, 1992. [林业局三北局. 中国三北防护林体系建设总体规划方案.银川: 宁夏人民出版社, 1993.]
[20] Prince S D, Brown de C E, Kravitz L. Evidence from rain use efficiencies does not support extensive SahelianDesertification. Global Change Biology, 1998, 4: 359-374.
[21] Wang J, Prince K P, Rich P M. Spatial patterns of NDVI in response to precipitation and temperature in theCentral Great Plains. International Journal of Remote Sensing, 2001, 22: 3827-3844.
[22] Zhu Xudong, He Honglin, Liu Min et al. Spatio-temporal variation characteristics of photosynthetically activeradiation in China in recent 50 years. Acta Geographica Sinica, 2010, 65(3): 270-280. [朱旭东, 何洪林, 刘敏等.近50 年中国光合有效辐射的时空变化特征. 地理学报, 2010, 65(3): 270-280.]
[23] Milich L, Wessis E. GAC NDVI images: Relationship to rainfall and potential evaporation in the grazing lands ofthe Gourma (northern Sahel) and in the croplands of the Niger-Nigeria border (southern Sahel). InternationalJournal of Remote Sensing, 2000, 21: 261-280.
[24] Hermann S M, Hutchinson C F. The changing contexts of the desertification debate. Journal of AridEnvironments, 2005, 63: 535-537.
[25] Chen Xunhao, Li Xiaobing, Shi Peijun. Variation in NDVI driven by climate factors across China.1983-1992. Chinese Journal of Plant Ecology, 2001, 25(6): 716-720. [陈云浩, 李晓兵, 史培军. 1983-1992 年中国陆地NDVI 变化的气候因子驱动分析. 植物生态学报, 2001, 25(6): 716-720.]

近25 年三北防护林工程区土地退化及驱动力分析

The Distribution and Drivers of Land Degradation in the Three-North Shelter Forest Region of China during 1982-2006

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